Single-carrier block transmission (SCBT) (also referred as SC-FDE) and orthogonal frequency domain multiplexing (OFDM) have been adopted in IEEE 802.11ad and 802.15.3c as two physical layer (PHY) transmission schemes. Comparing with OFDM, SCBT exhibits lower peak-to-average power ratio (PAPR), and is less sensitivity to carrier frequency offset (CFO). By employing frequency domain equalization (FDE), SC-FED has comparable receiver complexity with respect to OFDM, and the inter-symbol interference (ISI) caused by the multi-path effects can be well suppressed. Due to these facts, SC-FDE is being considered as one importance technique to support high data rate, high performance millimeter-wave (MMW) WLAN/WPAN operating at 60 GHz. Single-carrier frequency domain equalization (SC-FDE) has been standardized as an alternative of orthogonal frequency division multiplexing (OFDM) in IEEE 802.11ad and 802.15.3c, which are specifications regarding 60-GHz communications. In addition, with the use of low density parity-check (LDPC) codes, multi-Giga bits transmission can be supported in MMW WLAN/WPAN with desired decoding complexity.
In general, LDPC coding has following advantages over conventional Turbo coding, making it better suited for very high rate data transmission: stronger error detection and correction capability, lower decoding complexity, and more flexible coding rate and block length. However, in order to perform LDPC decoding in SC-FDE, log-likelihood ratio (LLR) has to be known as a priori. LLR calculation involves the estimation of effective channel gain and noise power, which will be changed due to FDE.
FIG. 1 is a block diagram showing a conventional SC-FDE receiver 100 for LDPC decoding. As shown in FIG. 1, a received data stream is first pulse filtered in pulse filter 101. Then, demultiplexer 103 de-multiplexes preambles from the payload data to facilitate channel estimation. Channel frequency response estimation unit 109 and noise variance estimation unit 111 estimate a channel frequency response (CFR) and a signal-to-noise ratio (SNR) according to the preambles, respectively. After guard interval (GI) removal in guard interval removal unit 105, FFT unit 107 performs fast Fourier transform (FFT) on the payload data to transfer it from the time domain to the frequency domain. Then, MMSE frequency domain equalizer 113 performs a minimum mean squared error FDE (MMSE-FDE) on the FFT-ed data to eliminate ISI based on MMSE-FDE coefficients which are calculated based upon the preamble estimated CFR and the SNR. We note that since MMSE-FDE exhibits much better performance than zero-forcing FDE (ZF-FDE), and we only consider MMSE-FDE here. It is obvious that ZF-FDE is also applicable. After that, IFFT unit 115 performs inverse FFT (IFFT) to transfer the data stream outputted from MMSE frequency domain equalizer 113 from the frequency domain back to the time domain. LLR calculation unit 117 calculates an LLR based on the preamble estimated CFR and the SNR, and forwards it to LDPC decoding unit 119, which performs constellation de-mapping and LDPC decoding to obtain the desired bit stream.
We assume s as the coded bit, where sε{0,1}. The LDPC coded bit stream is a symmetric binary sequence. Therefore, by referring to reference document 1, we have
                              q          =                                    log              ⁢                                                Pr                  ⁡                                      (                                                                  s                        =                                                  0                          |                                                      r                            ~                                                                                              ,                      α                                        )                                                                    Pr                  ⁡                                      (                                                                  s                        =                                                  1                          |                                                      r                            ~                                                                                              ,                      α                                        )                                                                        =                                          2                                                      σ                    ~                                    2                                            ⁢              α              ⁢                                                          ⁢                              r                ~                                                    ,                            (        1        )            where {tilde over (r)}, α and {tilde over (σ)}2 represent the received data symbols, effective channel gain and noise power, respectively. Since the preamble is only used for channel estimation but not equalized along with the payload data, the conventional method employed in IEEE 802.11ad/15.3c is to directly input the preamble estimated CFR and noise variance to the LLR calculation unit. As shown in FIG. 1, output from channel frequency response estimation unit 109 and noise variance estimation unit 111 are directly inputted in LLR calculation unit 117 for LLR calculation.
Obviously, this method fails to provide accurate soft information to the LDPC decoding as the channel frequency response and noise variance are changed due to FDE.
Reference document 2 proposes a virtual channel (VC) based LLR calculation method for LDPC decoding in SC-FDE. The VC based method proposed in reference document 2 utilizes a so-called Unique Word (UW), and takes the effect brought out by FFT, FDE and IFFT into account to track post effective channel condition.
FIG. 2 is a block diagram showing a receiver 200 for LDPC decoding by using the VC based LLR calculation method proposed in reference document 2. Pulse filter 201, demultiplexer 203, guard interval removal unit 205, FFT unit 207, channel frequency response estimation unit 209, noise variance estimation unit 211, MMSE frequency domain equalizer 213, IFFT unit 215, LLR calculation unit 217 and LDPC decoding unit 219 shown in FIG. 2 are similar to pulse filter 101, demultiplexer 103, guard interval removal unit 105, FFT unit 107, channel frequency response estimation unit 109, noise variance estimation unit 111, MMSE frequency domain equalizer 113, IFFT unit 115, LLR calculation unit 117 and LDPC decoding unit 119 shown in FIG. 1, and the detailed description thereof are omitted here.
As shown in FIG. 2, receiver 200 further comprises post effective channel gain estimation unit 221 and post effective noise power estimation unit 223 for estimating a post effective channel gain and a post effective noise power from the signals outputted from IFFT unit 215, respectively, and inputting the estimated post effective channel gain and post effective noise power to LLR calculation unit 217 to obtain LLR according to the equation (1).
FIG. 3 shows a conventional cyclic prefix (CP) based frame structure and a UW based frame structure, where TG, TD and TFFT represent the length of GI, data block and FFT block, respectively. The difference between conventional CP based frame structure and the UW based frame structures is pretty straightforward. In the conventional frame structure, CP is simply copied from the last part of each data block, as shown in FIG. 3. Hence, we have TFFT=TG. In the UW based frame structure, a pseudo-random bipolar sequence is inserted between two adjacent data blocks. Since each FFT block contains both the data block and the attached UW, we have TFFT=TG+TD. Therefore, the spectral efficiency per FFT block of UW is calculated as
                    η        =                                            T              D                                      T              FFT                                =                                                    T                D                                                              T                  D                                +                                  T                  G                                                      .                                              (        2        )            
Obviously, we can see that UW based frame structure is less spectrally efficient than CP based scheme (η=1) per FFT block.
Next, we briefly introduce the use of UW to estimate LLR parameters.
At the receiver end, the received time domain signal can be written asr(t)=x(t){circle around (×)}h(t)+n(t),  (3)where x(t) is the transmitted signal, h(t) denotes the channel impulse response (CIR) and n(t) is the additive white Gaussian noise with variance σ2. Here, {circle around (×)} represents the convolution product operation.
After FFT, FDE and IFFT shown in FIG. 2, the final output signal can be modeled as (assuming ISI is well suppressed by FDE){tilde over (r)}(t)≈αx(t)+ñ(t),  (4)where α and ñ(t) denote the post effective channel gain and the post effective noise with variance {tilde over (σ)}2. After down-sampling, we can rewrite (4) in discrete forms, i.e.,{tilde over (r)}k≈αxk+ñk,  (5)where k represents the time index.
As mentioned above, a UW consists of a pseudo-random bipolar sequence with elements from {+1,−1}. Depending on the original sign (+ or −), the equalized UW can be categorized into “+” sub-sequence, i.e.,{tilde over (r)}k+≈α(+1)+ñk+,  (6)and “−” sub-sequence{tilde over (r)}k−≈α(−1)+ñk−,  (7)respectively. By referring to reference document 2, the post effective channel gain and noise power can be estimated via UW as
                              α          =                                                    E                ⁡                                  (                                                            r                      ~                                        k                    +                                    )                                            -                              E                ⁡                                  (                                                            r                      ~                                        k                    -                                    )                                                      2                          ,                                  ⁢        and                            (        8        )                                                      σ            ~                    2                =                                                            E                ⁡                                  [                                                            (                                                                                                    r                            ~                                                    k                          +                                                -                                                  E                          ⁡                                                      (                                                                                          r                                ~                                                            k                              -                                                        )                                                                                              )                                        2                                    ]                                            +                              E                ⁡                                  [                                                            (                                                                                                    r                            ~                                                    k                          -                                                -                                                  E                          ⁡                                                      (                                                                                          r                                ~                                                            k                              -                                                        )                                                                                              )                                        2                                    ]                                                      2                    .                                    (        9        )            
By substituting (8) and (9) into (1), the LLR is obtained for LDPC decoding.
It can be seen from the introduction above that the performance improvement obtained by a VC based scheme is limited due to the limited length of UW, the SNR degradation caused by the insertion of UW, the lower spectral efficiency compared with the conventional scheme. These occur because the UW occupies the bandwidth prepared for the actual payload data transmission.
Therefore, there needs a novel LDPC decoding method which improves error correction performance while keeps the system spectral efficiency.
Reference Documents
[1] J. Hou, P. H. Siegel, and L. B. Milstein, “Performance analysis and code optimization of low density parity-check codes on Rayleigh fading channels” in IEEE Journal on Selec. Areas in Comm., vol. 19, May 2001, pp. 924-934;
[2] M. Lei, S. Zhang, K. Chen, Y. Huang, X. Wu, and L. Yan, “Virtual channel based LLR calculation for LDPC coded SC-FDE system in 60-GHz WPAN” in IEEE Globecom 2008, December 2008.